دانلود کتاب Genetic and Evolutionary Computation: Medical Applications

Genetic and Evolutionary Computation: Medical Applications provides an overview of the range of GEC techniques being applied to medicine and healthcare in a context that is relevant not only for existing GEC practitioners but also those from other disciplines, particularly health professionals. There is rapidly increasing interest in applying evolutionary computation to problems in medicine, but to date no text that introduces evolutionary computation in a medical context. By explaining the basic introductory theory, typical application areas and detailed implementation in one coherent volume, this book will appeal to a wide audience from software developers to medical scientists. Centred around a set of nine case studies on the application of GEC to different areas of medicine, the book offers an overview of applications of GEC to medicine, describes applications in which GEC is used to analyse medical images and data sets, derive advanced models, and suggest diagnoses and treatments, finally providing hints about possible future advancements of genetic and evolutionary computation in medicine. Explores the rapidly growing area of genetic and evolutionary computation in context of its viable and exciting payoffs in the field of medical applications. Explains the underlying theory, typical applications and detailed implementation. Includes general sections about the applications of GEC to medicine and their expected future developments, as well as specific sections on applications of GEC to medical imaging, analysis of medical data sets, advanced modelling, diagnosis and treatment. Features a wide range of tables, illustrations diagrams and photographs. Content: Chapter 1 Introduction (pages 1–2): Chapter 2 Evolutionary Computation: A Brief Overview (pages 3–15): Stefano Cagnoni and Leonardo VanneschiChapter 3 A Review of Medical Applications of Genetic and Evolutionary Computation (pages 17–43): Stephen L. SmithChapter 4.1 Evolutionary Deformable Models for Medical Image Segmentation: A Genetic Algorithm Approach to Optimizing Learned, Intuitive, and Localized Medial?Based Shape Deformation (pages 46–67): Chris McIntosh and Ghassan HamarnehChapter 4.2 Feature Selection for the Classification of Microcalcifications in Digital Mammograms Using Genetic Algorithms, Sequential Search and Class Separability (pages 69–84): Santiago E. Conant?Pablos, Rolando R. Hernandez?Cisneros and Hugo Terashima?MarinChapter 4.3 Hybrid Detection of Features within the Retinal Fundus Using a Genetic Algorithm (pages 85–109): Vitoantonio Bevilacqua, Lucia Cariello, Simona Cambo, Domenico Daleno and Giuseppe MastronardiChapter 5.1 Analysis and Classification of Mammography Reports Using Maximum Variation Sampling (pages 112–131): Robert M. Patton, Barbara G. Beckerman and Thomas E. PotokChapter 5.2 An Interactive Search for Rules in Medical Data Using Multiobjective Evolutionary Algorithms (pages 133–148): Daniela Zaharie, D. Lungeanu and Flavia ZamfiracheChapter 5.3 Genetic Programming for Exploring Medical Data Using Visual Spaces (pages 149–172): Julio J. Valdes, Alan J. Barton and Robert OrchardChapter 6.1 Objective Assessment of Visuo?Spatial Ability Using Implicit Context Representation Cartesian Genetic Programming (pages 174–189): Michael A. Lones and Stephen L. SmithChapter 6.2 Towards an Alternative to Magnetic Resonance Imaging for Vocal Tract Shape Measurement Using the Principles of Evolution (pages 191–207): David M. Howard, Andy M. Tyrrell and Crispin CooperChapter 6.3 How Genetic Algorithms can Improve Pacemaker Efficiency (pages 209–221): Laurent Dumas and Linda El AlaouiChapter 7 The Future for Genetic and Evolutionary Computation in Medicine: Opportunities, Challenges and Rewards (pages 223–227):